Pareto Optimal Streaming Unsupervised Ensemble Learning Soumya - PowerPoint PPT Presentation

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Pareto Optimal Streaming Unsupervised Ensemble Learning Soumya Basu University of Texas at Austin Steven Gutstein (ARL), Brent Lance (ARL), and Sanjay Shakkottai (UT Austin) Poster # 178 Streaming Unsupervised Ensemble Learning Po Poster #178

Pareto Optimal Streaming Unsupervised Ensemble Learning Soumya Basu University of Texas at Austin Steven Gutstein (ARL), Brent Lance (ARL), and Sanjay Shakkottai (UT Austin) Poster # 178
Streaming Unsupervised Ensemble Learning Po Poster #178 Agents: Neural Networks and Humans tasks Deterministic Labeling • Unknown Confusion matrices • Tasks: Stream of unlabeled images for labeling Resource Allocation and Label Aggregation: 1. Each image is sequentially routed to subset of agents 2. Collected labels are continually aggregated Routing: Online routing based on ALL the collected labels Exit: Image exits with a final label only if ‘accuracy is high’ or ‘all labels are collected’ agents Image credits: CIFAR-10, A. Krizhevsky, Online Learning: Explore-exploit learning of confusion matrices 2009; thenounproject.com, (NNs - K. M. Synstad; Faces - A. Selimov)
Pareto Optimality Po Poster # 178 Accuracy vs Rate Tradeoff Low arrival rate = Large number of Dataset: agents per image Grouped Cifar-10 = High Accuracy Ensemble: Three AlexNet High arrival rate One VGG-19 = Small number of Two ResNet18 agents per image = Low accuracy Contributions • Queue-based architecture for dynamic routing • Online tensor decomposition for learning confusion matrices • Provably supports any point in the Pareto region

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